First, we optimize the parameters of the features on EmoDB. We perform experiments on this corpus using leave-one-speaker-out (LOSO) cross validation by keeping one speaker in test. Out of the remaining speakers, we use two of them in validation and seven in training. We also apply five-fold data augmentation by corrupting training set with additive noises and room reverberation effect following the Kaldi recipehttps://github.com/kaldi-asr/kaldi/tree/master/egs/voxceleb/v2 for x-vector training [1]}.
